4S
INTELLIGENCE AND DECISION
SUPPORT SYSTEMS
Since the establishment of computers as business tools, designers have planned for the
day when systems could work on their own, either as decision makers or as partners in
the decision-making effort. Computers such as these would use "artificial intelligence." In
this context, we are using the term
artificial intelligence
to mean the emulation of human
expertise by the computer through the encapsulation of knowledge
in a particular domain
and procedures for acting upon that
knowledge.
The advantage of such artificial intelligence
is that the computers would not
be
prone to the forgetfulness, bias, or distractions that plague
human decision makers. Such systems would help us make better decisions, protect us from
unanticipated events, and even provide companionship of a sort as the computer played
games such as chess with us. Unfortunately, many factors ranging from unreasonable
expectations to insufficient developments in hardware stood in the way of this goal.
During the 1980s, when smaller, faster processors and storage media were first be-
coming available, many thought the area of "expert systems" would provide a focused
use of artificial intelligence and solve problems that usually could be tamed only by an
expert or group of
experts,
because they required a human reasoning process. This required
computers to use symbols in the analysis and to understand, interpret, and manipulate the
symbols just as humans do. Such systems would address problems normally requiring an
individual to amass large amounts of data and knowledge about a field and process those
data using sophisticated reasoning as well as accepted rules of thumb.
For example, early uses of expert systems provided diagnostic assistance to physicians.
CADUCEUS,
developed at Carnegie Mellon University, provided medical diagnosis of
internal medicine problems, and
MYCIN,
developed at Stanford University, provided
Decision Support Systems for Business Intelligence
by Vicki L. Sauter
Copyright © 2010 John Wiley & Sons, Inc.
INTELLIGENCE AND DECISION SUPPORT SYSTEMS
diagnostics regarding blood diseases. As design and implementation technologies
improved, expert systems moved to business applications. Digital Equipment Corporation
deployed
XCON,
an expert system to construct systems by determining the set of wires,
cabinets, and parts necessary to meet the user's computing needs. Similarly, Peat Marwick
developed
Loan Probe
to assist auditors in assessing commercial banks' loan losses and
reserves, so as to help auditors determine whether the banks could cover bad
debt.
American
Express used
Authorizer's Assistant
to facilitate quick and consistent credit authorization.
Oxiscan,
developed by Oxicron Systems, analyzed market data for product managers by
performing statistical analyses on scanner data and then interpreting the results.
Although expert systems were successful from a technological perspective, they were
not accepted from a managerial perspective. The proof managers needed about the effec-
tiveness of the systems was not available. In addition, many such systems were developed
on specialized, stand-alone hardware that did not interface with any existing data or appli-
cations. As a result, they never were integrated into the business plan.
The technology was established, however. The current trend is to embed artificial
intelligence and expert system tools into DSS. For example, the U.S. Army uses embedded
expert systems in its logistics planning. Similarly, Putnam has embedded intelligence into
its trading software to monitor for compliance with regulations. In fact, a recent survey by
the Commerce Department indicated that more than 80% of the Fortune 500 companies use
some form of artificial intelligence in their operations. The intelligence might be embedded
into the DSS to help select what data should be analyzed or how the data should be analyzed.
Similarly, artificial intelligence might help decision makers to complete sensitivity analyses
to ensure that all aspects of the problem have been examined. It might identify aspects of
the problem that have been overlooked and relate the current findings to previous analyses
or data. Instead of replacing the decision maker, the artificial intelligence is built into the
DSS to help the decision maker exploit trends found in the data more easily.
Many DSS include features that facilitate data mining, as discussed in the previous
chapter. Through the help of artificial intelligence and statistical analyses, these features
find information from existing data. In addition, the system determines how to present
that new knowledge so that it is understandable to humans. Other DSS use embedded
neural networks that are trained by examples to recognize patterns and aberrations. For
examples, changes in purchasing patterns might identify credit cards that are stolen. In
fact, MasterCard Worldwide pioneered their use so minimize the time thiefs can use the
cards.
Still other systems provide hybrid applications of a variety of artificial intelligence
tools.
For example, combinations of tools that derive conclusions from data and perform
inductive reasoning facilitate DSS that provide support for the convertible-bond market.
Over time, almost all DSS will include some kind of artificial intelligence. At present,
artificial intelligence tends to be associated with choices needing some expertise where the
expert is not always available or is expensive, where decisions are made quickly, and where
there are too many possibilities for an individual to consider at one time and there is a high
penalty associated with missing one or more factors. Artificial intelligence is helpful too
when consistency and reliability in judgments are the paramount goal, not creativity in the
choice process.
Currently the greatest promise lies in hybrid systems that combine both expert systems
and neural nets. The capture and preservation of human expertise is best done by expert
systems, but they, like humans, do not adjust to changes readily. Neural nets, on the other
hand, are not good repositories for human expertise, but they are trained to continue to
learn. They can examine large amounts of data and find causal relationships that help them
adapt to changes in their environment. Together, the two technologies can provide ongoing
support within a DSS.
INTELLIGENCE AND DECISION SUPPORT SYSTEMS 199
Modeling Insights
Deep Blue
The acceptance of artificial intelligence has not been universal· Some managers just do not
trust the computers to understand all of the interworkings of the choice context. Other managers
have concerned about the legal ramifications of
a
wrong choice.
Still other decision makers just do not believe in the reasoning process of computers. One
example of this disbelief was expressed by Garry Kasparov when he defended his World Chess
Champion position against Deep Blue, an IBM computer programmed to play chess, fn the first
game of the match, the compute
r made a move that Kasparov judged to be "a wonderful and
extremely human move." However, Kasparov had difficulty responding to the move because a
computer "would never make such a move." Kasparov judged that although humans regularly
see the impact, "a computer can't 'see' the long-term consequences of structural changes in the
position or understanding how changes in pawn formations may be good or bad/*
In fact, he was so sure that the computer could not reason that he was ^stunned" by the move.
While he had played chess against many computers before Deep Blue, this move caused him to
"feel - ϊ could
smell
- a new kind of intelligence across the table.'* Unfortunately for Kasparov,
the computer had, in fact, psyched him out with the move and actually won the game,
Kasparov, however, showed that the human's intelligence was still superior because the
experience forced him to think of the shortcomings of computers throughout the remainder of the
match and use that information strategically in his play development. For example, he changed
moves in a we]
1
known opening sequence in one game. Since the new opening was not stored in
the database, Deep Blue could not find an appropriate plan to respond to it. Neither could Deep
Blue reason that Kasparov's change from the well-known sequence was meaningless and respond
with a known response. In the end, Kasparov won the tournament in 1996 and kept his title,
However, IBM heavily upgraded Deep Blue to improve its logic. Later in 1997, Deep Blue
won a six-game match by two wins to one with three draws. Kasparov claimed there was cheating
and demanded a rematch, but IBM decline
d and disassembled Deep Blue.
Deep Blue was a combination of special purpose hardware and software with an IBM
RS/6000 SP2 - a system capable of examining 200 million moves per second, or 50 billion
positions, in the three minutes allocated for a single move in a chess game.
Deep Blue vs. Kasparov 1996, game 1
nnni
■ ■ ■ ■
The chess game image is from Wikipedia Commons. The file is licensed under the Creativ
e
Commons Attribution Share Alike 3.0 License,
200
INTELLIGENCE AND DECISION SUPPORT SYSTEMS
To
build artificial intelligence into the system, two primary topics need to be addressed:
how
to
program "reasoning" and what
to
do with uncertainty in the decision-making context.
These will be addressed in the next two sections.
PROGRAMMING REASONING
The reasoning process in humans is often automatic or implicit, and hence it is difficult to
see how it might be programmed in a set of deliberate steps for a computer. If, however,
we examine the reasoning process slowly and deliberately through its individual steps so
that we can see how the computer completes the reasoning process. Actually, reasoning by
both humans and computers must take one of
two
basic approaches. Either we begin with a
goal and try to prove that it is true with the facts we have available or we begin with all the
"known facts" and try to prove as much as we can. In computer terms, these are referred
to as backward reasoning and forward reasoning, respectively. The following examples
demonstrate deliberate examples of backward and forward reasoning and the manner in
which intelligence can be built into a DSS. Both examples will use the same information
so as to illustrate the differences in the processes.
Suppose there is a set of facts, known as facts A, B, C, D, E, F, G, and H. All these
facts are logical facts, and they can be set to either "true" or "false." In addition, there are
certain known relationships among the facts. These are listed below in the order in which
they might appear in the code:
Rl:
>
IF Fact E and Fact M and Fact G are all true, then
Fact F is true;
IF Fact K and Fact E are both true, then Fact D is true;
IF Fact N is true, then Fact Y is true;
IF Fact Y is true, then Fact H is true;
IF Fact B and Fact G are both true, then Fact M is true;
IF Fact K and Fact F are both true, then Fact Y is true;
IF Fact K is true, then Fact B is true.
R2:
R3:
R4:
R5:
R6:
R7:
>
>
>
>
>
>
Researchers are investigating prospective logic as a way to program morality into a computer.
Using prospective
logic,
programmers can
model
a
moral dilemma
so the
computer
can
determine
the logical outcomes of
all
possible decisions and select the best (or least worst)
one.
This sets the
stage
for
computers
that have
"ethics,
1
'
which
could
allow
fully autonomous
machines
programmed
to
make judgments
based on a human
moral foundation. Currently
two
researchers
have
developed
a system capable
of working through
the
"trolley
problem,"
an ethical
dilemma proposed
by
British
philosopher Philippa Foot in the 1960s, In this dilemma, a runaway trolley is about to hit five
people tied
to the
track,
but the
subject
can hit a
switch that will
send the
trolley onto another
track
where only one person is tied down. The prospective logic program can consider each possible
outcome based on different scenarios and demonstrate logically what the consequences of its
decisions might be.
PROGRAMMING REASONING
201
The ways in which these relationships are processed are quite different with backward and
forward chaining.
Backward-Chaining Reasoning
In backward chaining, we begin with a goal and attempt to prove it. For example, suppose
the goal is to prove that fact H is true. The system will process the relationships beginning
with the first one it encounters proving the goal (in this case, fact H) to be true:
Rl;
> IF Fact E and Fact M and Fact G are all true, then
Fact F is true;
R2
:
>
IF Fact K and Fact E are both true, then Fact D is true;
R3
:
>
IF Fact N is true, then Fact Y is true?
■■^
IF
FM^HHHBi
then Fact
SHHH
R5:
> IF Fact B and Fact G are both true, then Fact M is true;
R6:
> IF Fact K and Fact F are both true, then Fact Y is true;
R7:
>
IF
Fact
K is
true, then Fact
B is
true,
In order to prove relationship 4, it is necessary to prove that fact Y is true. Hence» proving
that fact Y is tme is now the goal of the system. It will again process rules:
Rl:
>* IF
Fact
E and
Fact
M and
Fact
G are all
true, then
Fact
F is
true;
R2
:
>
IF
Fact
K and
Fact
E are
both true, then Fact
D is
true;
R3:
>
IF Fact N is
true,
then Fact Y is true;
R4:
>
IF
Fact
Y is
true, then Fact
H is
true;
R5:
>
IF Fact B and Fact G are both true, then Fact M is true;
R6:
> IF Fact K and Fact F are both true, then Fact Y is true;
R7:
> IF Fact K is true, then Fact B is true.
To prove relationship 3, it is necessary to prove that fact N is true. We can see from the
seven relationships that there is nothing from which the system can infer whether fact N is
true.
Hence, the system is forced either to use a default value (if one is specified) or ask
the user Suppose there is no default value given, and the user does not know whether fact
N is true. Under these circumstances, the system is
unable
to infer that fact N is true, so it
assumes
nothing
about the validity of fact N. However, it must locate another relationship
in order to infer fact Y is true:
Rl:
>
IF
Fact
E and
Fact
M and
Fact
G are all
true, then
Fact
F is
true;
R2
:
>
IF
Fact
K and
Fact
E are
both true, then Fact
D is
true;
R3
:
>
IF Fact N is true, then Fact Y is true;
R4:
>
IF
Fact
Y is
true, then Fact
H is
true;
R5:
>
IF
Fact
B and
Fact
G are
both true, then Fact
M is
true;
R6:
>
IF Fact K and Fact F are both true, then Fact Y is true;
R7:
>■ IF Fact K is true, then Fact B is true.
To prove relationship 6, it is necessary to prove that facts K and F are true. The system
begins with trying to prove fact K. As with fact N, mere are no relationships from which
one can infer that fact K is known. The system then must use a default value (if one is
202 INTELLIGENCE AND DECISION SUPPORT SYSTEMS
specified) or ask the user. Suppose in this case the user knows that fact K is true, and hence
the system attempts to prove that fact F is true:
Rl
R2
R3
R4
R5
R6
R7
>*
>
>
ί
>
>
:
>
: >
IF Fact
Fact F
IF Fact
IF Fact
IF Fact
IF Fact
IF Fact
IF Fact
E
is
K
N
Y
B
K
K
and
Fact
true ;
and Fact
is true,
is true,
and Fact
and Fact
is true,
M and Fact
G are all true
H
E are both true, then Fact
then Fact Y
is true;
then Fact H is true;
G are both true, then Fact
F are both true, then Fact
then Fact B is true.
then
D is
M is
Y is
true;
true ;
true;
As with fact N, there are no relationships from which one can infer the value of fact E
(whether or not it is true)- The system then must use a default value (if one is specified) or
ask the user. Suppose in this case the user knows that the value of fact E is known as true,
and hence the system attempts to prove that fact M is true:
Rl:
> IF Fact E and Fact M and Fact G are all true, then
Fact F is true;
R2:
>
IF Fact K and Fact E are both true, then Fact D is
R3:
> IF Fact N is true, then Fact Y is true;
R4:
> IF Fact Y is true, then Fact H is true;
R5;
>-
IF Fact B and Fact G are both true, then Fact M is
R6
:
> IF Fact K and Fact F are both true, then Fact Y is
R7
:
>* IF
Fact
K is
true, then Fact
B is
true,
The first step in that process is to establish that fact B is true:
Rl:
>
IF Fact E and Fact M and Fact G are all true, then
Fact F is true;
R2
:
>
IF
Fact
K
and
Fact E are both true, then Fact D is true;
R3
:
>■ IF Fact W is true, then Fact Y is true;
R4:
>
IF Fact Y is true, then Fact H is true;
R5:
> IF Fact B and Fact G are both true, then Fact M is true;
R6:
> IF Fact K and Fact F are both true, then Fact Y is true;
R7:
>
IF Fact K is true, then Fact B is true.
Relationship 7 states that fact B is true if fact K is true. Earlier, the system asked the user
and determined that fact K is true. At that time the value was stored» and hence the system
need not query the user again. Hence fact B is true, and the system can proceed to attempt
to determine whether fact G is true, As was true with fact N, there are no relationships from
which we can infer the value of fact G (whether or not it is true). The system then must use
a default value (if one is specified) or ask the user. Suppose in this case the user knows that
fact G is known. Hence, the system now establishes that fact M is true, since facts B and
G have been established as true. The system again returns to processing relationship
1
and
establishes that fact F is true:
Rl:
> IF Fact E and Fact M and Fact G are all true, then
Fact F is true;
R2:
> IF Fact K and Fact E are both true, then Fact D is true;
R3:
>
IF Fact N
is true, then Fact Y is true;
R4:
>
IF Fact Y is true, then Fact H is true;
true;
true;
PROGRAMMING REASONING 203
R5:
> IF Fact B and Fact G are both true, then Fact M is true;
R6:
>
IF
Fact K and Fact F are both true, then Fact Y is true;
R7:
> IF Fact K is true, then Fact B is true.
With this information, the system return« to processing relationship 6 and establishes that
fact Y is true;
Rl:
> IF Fact E and Fact M and Fact G are all true, then
Fact F is true;
R2
R3
K4
R5
R6
R7
>
>
>
>
>
>
IF
IF
lr
IF
IF
IF
Fact
Fact
Fact.
Fact
Fact
Fact
K
N
Y
B
K
K
and Fact
is true,
is true,
and Fact
and Fact
is true
H
E are both true, then
then Fact Y is true?
then Fact H is true;
G are both true, then
F are both true, then
then Fact B is true,
Fact
Fact
Fact
D
K
Y
is
is
is
true;
true;
true;
R2
R3
R4
R5
R6
R7
>
>
>
>
>
>
D is
M is
Y is
true;
true;
true;
Since fact Y is true,
the
system can establish that fact H is true through relationship 4;
Rl
:
> IF Fact E and Fact M and Fact G are all true, then
Fact F is true;
IF Fact K and Fact E are both true, then Fact
IF Fact W is true, then Fact Y is true;
IF Fact Y is true,
then Fact H is true;
IF Fact B and Fact G are both true, then Fact
IF Fact K and Fact F are both true, then Fact
IF Fact K is true, then Fact B is true.
Since establishing that fact H is true is the goal of the system, it would stop processing at
this point and find no additional information. This process is illustrated in Figure 4S.1.
Forward-Chainin
g Reasoning
Consider, now, the path lhaL is followed using forward chaining. Using this system, we
begin with information and attempt to learn as much as possible. For example, suppose
we begin by knowing that facts K and E are both true. The system will look to prove
any relationship possible given these two facts and hence process relationships 2 and 7
(sequentially in the order in which they appear in the code):
Rl:
>
IF Fact E and Fact M and Fact G are all true, then
Fact F is true;
IF Fact K and Fact E are both true, then Fact D is true;
IF Fact N is true, then Fact Y is true;
IF Fact Y is true, then Fact H is true;
IF Fact B and Fact G are both true, then Fact M is true;
IF Fact K and Fact F are both true, then Fact Y is true;
IF Fact K is true, then Fact
B is true.
The environment changes as a result of this processing, and the system now knows that
facts D and
B
are also
true.
Hence, the system considers all relationships again to determine
whether more information can be gleaned. However, there are no additional relationships
thai can be processed. Unlike the case in backward chaining, the system does not begin to
R2;
R3:
R4:
^:
R6:
R7;
>
>
>
>
>
>
204
INTELLIGENCE AND DECISION SUPPORT SYSTEMS
Figure 4S.1. Hierarchy of logic—backward chaining.
prompt the user for information that might allow it to go further, and hence it would stop
and learn no additional facts.
Some software lets developers use hybrid approaches to programming by allowing
procedural programming, access programming, and/or object-oriented programming in
addition to forward- and/or backward-chaining pathways. Consider the forward-chaining
example above. Suppose the access programming code specified that users should be
queried, or a database should be searched, or a default value should be set if the status of
fact G is not known by this point of processing. If the user or database indicated fact G
were true, the system would again invoke the forward-chaining component and it would
process relationship 5:
PROGRAMMING REASONING 205
R2:
R3:
R4:
R5:
R6:
R7
:
>
>
>
>
>
>
Rl:
>
IF
Fact
E and
Fact
M and
Fact
G are all
true, then
Fact
F is
true;
IF Fact
K and
Fact
E are
both true, then Fact
D is
true?
IF Fact
N is
true, then Fact
Y is
true;
IF Fact
Y is
true, then Fact
H is
true;
IF Fact
B and
Fact
G are
both true, then
Fact
M is
true;
IF Fact
K and
Fact
F are
both true, then Fact
Y is
true;
IF Fact
K is
true
H
then Fact
B is
true.
The information regarding fact M would cause the system to evaluate all relationships
that require some or all of facts K, E, N, B, or M to be true, and hence it would process
relationship 1;
Rl:
> IF
Fact
E and
Fact
M and
Fact
G are all
true, then
Fact
F is
true;
E
are
both true, then Fact
D is
true;
then Fact
Y is
true;
then Fact
H is
true;
G
are
both true, then Fact
M is
true;
F
are
both true, then Fact
Y is
true;
then Fact
B is
true.
The new information about fact F requires the system to reevaluate the relationships to
determine whether more information can be learned, and hence it will seek any relation-
ship that includes fact F and some subset of the other facts known at this time, as in
relationship 6:
Rl:
>
IF
Fact
E and
Fact
M and
Fact
G are all
true, then
Fact
F is
true;
R2
R3
R4
R5
R6
R7
>
>
>
>
>
>
IF
TF
TF
IF
IF
IF
Fact
Fact
Fact.
Fact
Fact
Fact
K
N
Y
B
K
K
and Fact
is true,
is true,
and Fact
and Fact
is true,
R2
R3
R4
R5
R6
R7
>
>
>
>
>
>
IF
IF
IF
IF
IF
IF
Fact
Fact
Fact
Fact
Fact
Fact
K
N
Y
B
K
K
and Fact
is true,
is true,
and
Fact
and Fact
is true,
E
are
both true, then
then Fact
Y is
true;
then Fact
H is
true;
G
are
both true, then
F
are
both true, then
then Fact
B is
true.
Fact
Fact
Fact
D
M
Y
is true;
is true;
is true;
The process proceeds in a similar fashion now that fact Y is known. Hence, the system wilt
process relationship 4:
Rl:
>
IF
Fact
E and
Fact
M and
Fact
G are all
true, then
Fact
F is
true;
IF Fact
K and
Fact
E are
both true, then Fact
D is
true;
IF Fact
N is
true, then Fact
Y is
true;
IF Fact
Y is
true, then Fact
H is
true;
IF Fact
B and
Fact
G are
both true, then Fact
H is
true;
IF Fact
K and
Fact
F are
both true, then Fact
Y is
true;
IF Fact
K is
true, then Fact
B is
true.
Since none of the relationship
s indicate any new knowledge can be gained by knowing that
fact H is true, the system would stop with this knowledge. This process is illustrate
d in
Figure 4S.2.
R2
R3
R4
R5
R6
R7
>
>
>
>
>
>
206
INTELLIGENCE AND DECISION SUPPORT SYSTEMS
Figure 4S.2. Hierarchy of logic—forward chaining.
Comparison of Reasoning Processes
In this example, the system "learned" the same ultimate fact (fact H is true) with backward
chaining and forward chaining
only when
forward chaining was supplemented by access
programming. However, the forward chaining with access programming and the pure
forward chaining process the relationships in quite different order. It is important to note
this for two
reasons.
First, the designer could find himself or herself with a dormant analysis
system unless information is sought in a particular manner. For example, suppose the last
example were done completely as a forward-chaining example (no access programming
interrupt). In this case, the system would quit processing after it learned that fact B was
true,
and there would be no way to push it to do more. The system would not perform as
the designers had envisioned or as the decision makers need.
Second, we should be concerned about the way in which the system seeks information
from the user for the sake of sustaining the confidence of the decision maker (sometimes
referred to as "face" validity). Decision makers expect information to be sought in a
particular order. If there are vast deviations from such a logical order, then decision makers
may question the underlying logic of the system. If the logic can be defended, then such
questioning helps the decision maker to reason more effectively. On the other hand, if
decision makers cannot establish why such reasoning has occurred, they might choose to
drop the DSS.
UNCERTAINTY
Decisions are difficult to make because of
uncertainty.
Decision makers are uncertain about
how outside entities will change their environments and thus influence the success of their
choices. In addition, sometimes decision makers are uncertain about the reliability of the
information they use as the basis for their choices. Finally, decision makers are uncertain
about the validity of the relationships that they believe govern the choice situation.
UNCERTAINTY
Often decision makers also need to interact with "fuzzy logic." The term
fuzzy logic
does not apply to a muddled thought process. Rather it means a method of addressing data
and relationships that are inexact. Humans address fuzzy logic regularly whenever they do
not treat decisions as totally "black-and-white" choices. The gradations of gray provide
flexibility in approaching problems that forces us to consider all possible options.
Consider, for example, whether a person is "tall." The term
tall
is a vague term that
means different things to different people. If in the choice process one selection procedure
required the machine to select only applicants who were tall, it would be difficult for the
DSS to do. Even in a sport such as basketball, where being tall really matters, the term
tall depends on the position one is playing. A particular individual might be tall if playing
guard but not if playing center because the requirements of the positions are so different.
Even if the discussion is limited to the position of guard, what is considered "tall enough"
is dependent upon other factors. In 1994 Mugsy Boggs, a basketball guard, was only 5 feet,
4 inches, which even I
1
do not consider tall. However, because he had fabulous technique,
he was considered tall enough to play that position.
Similarly, when trying to select among employment opportunities, we might employ
fuzzy
logic.
There is not one opportunity that is "good" and another that is "bad." Generally,
they are all somewhat good on some dimensions and somewhat bad on other dimensions.
It is difficult for most people to define what dimensions are most important in a reliable
way, but they can tell which opportunities are better than others. This illustrates the historic
problem that humans could make better decisions than computers because they could
address uncertainty in their reasoning processes.
So,
if DSS are to have "intelligence" that facilitates the choice processes, they must
also be able to address uncertainty from a variety of perspectives. There are two major
processes by which uncertainty is addressed in intelligent systems, with probability theory
and with certainty factors. These will be introduced separately.
^hat which is considered tall also depends upon how tall an individual
is.
Since
I
fall into
a
category
generally referred to as "short," I have a more liberal definition of
tall
than do other people.
Design Insights
The Turing Test
The
''standard interpretation" of
the Turing
Test,
in which player
C,
the
interrogator,
is
tasked with
trying
to
determine which player
- A
or
B
- is
a
computer and which is
a
human.
The interrogator
is limited to only using the responses to written questions in order to make the determination.
The
Turing Test
image
is
from Wikimedia
Commons.
The
file
is licensed under
the
Creative
Commons Attribution Share Alike 3.0 License.
208
INTELLIGENCE AND DECISION SUPPORT SYSTEMS
Representing Uncertainty with Probability Theory
Probability theory, which is the foundation of most of the statistical techniques used in
business applications, is based upon the belief that the likelihood that something could
happen is essentially the ratio of the number of successes to the number of possible trials.
So,
for
example,
if we flip a coin
100
times,
we expect 50 of those times to show "heads" and
hence we estimate the probability of heads as being
\.
Since few business situations are as
simple as flipping a coin, there are a variety of rules for combining probabilistic information
for complicated events. Furthermore, since we may update our estimates of probabilities
based upon seeing additional evidence, probabilists provide systematic methods for making
those changes in the estimates. This is referred to as Bayesian updating.
Consider the following example. Let us define three events, which we will call events
A, B, and C:
Event A: The act of being a good writer.
Event B: Receipt of an A in a writing course.
Event C: Receipt of an A in a systems analysis course.
Suppose:
P(A) =
0.5
P{A') =
0.5
P(A
Π
B)
= 0.24
P(Ai)BnC) =
0.015
P(B)
= 0.3
P{B') =
0.7
P(A DC) =
0.06
P(C)
= 0.1
P(C)
= 0.9
P(BDC) =
0.02
Without any new information, we believe the likelihood of being a good writer (event A) is
0.50. If, however, we know the person received an A in his or her writing class (event B),
we could
update
the probability the person is a good writer by applying Bayes' Rule:
P(A
Π
B)
0.24
P(A\B)
= — = = 0.80
v
'
' P(B)
0.30
That
is,
given this new information, we now believe fairly strongly that the person is a good
writer.
If, instead, the probability of the intersection between events A and B (that is, the
probability that the person is
both
a good writer and received an A in a writing course)
were quite low, such as 0.01, the conditional probability
P(A\B)
would be reduced sub-
stantially from the initial estimate to a value of
0.033.
That means we can update an initial
estimate after we get new information by either increasing or decreasing our certainty in
the likelihood of an event depending upon the new information provided.
A more generalized form of the equation is
Ρ(ΑΠΒ) Ρ(Β\Α)
P(A\B)=
v
'
'
P(A
Π
B)
+
P{A'
Π
B) P(B\A)P(A)
+
P{B\A')P{A
f
)
Suppose we now have the information that the person also received an A in his or
her systems analysis class. Based upon our earlier information, we could now update
UNCERTAINTY
209
the probability further:
P{A Π(ΒΠ
O)
Ρ(ΑΓ)ΒΓ) C)
P(A\B
Π
C) = — = —
P(BnC) P(BC\C)
P(BnC\A)P(A)
~
P(B
Π
C\A)P(A)
+
P{B
Π
C\A')P(A
f
)
Hence, given all the information available, we believe the likelihood that the person is a
good writer is 0.75.
Updating the rules using a Bayesian approach is similar to this process.
Representing Uncertainty with Certainty Factors
A popular alternative for addressing uncertainty is to use certainty factors. Instead of
measuring the likelihood as one function, we need to estimate a measure of
"belief"
separate
from a measure of
"disbelief."
New evidence could increase (decrease) our measure of
belief,
increase (decrease) our measure of
disbelief,
or have some impact on our measure of
both belief and
disbelief.
Its effect is a function of whether the information is confirmatory,
disconfirmatory, or both confirmatory of
one
and disconfirmatory of
the
other. Consider the
example shown above. Suppose you believe the subject to "be a good writer." You know
the person waived his or her writing course. This information would cause you to increase
your measure of belief that the person was a good writer but would have no impact on your
measure of
disbelief.
However, if you knew that the person received a C in the writing class
and almost everyone waived the writing class, this would have two effects. First, it would
increase your disbelief that the person was a good writer because he or she received a grade
of C in a class that most people waived. In addition, it would decrease your belief that the
Design insights
AI:
A Space Odyssey
HAL 9000 is a fictional computer in Arthur C. Clarke's
2001: A Space Odyssey.
The computer
was a powerful representation of artificial intelligence; HAL was programmed to insure the
success of the mission. It was capable of maintaining all systems on the voyage, of reasoning and
speech, facial recognition, and natural language processing,
as
well
as
lip reading, art appreciation
interpretin
g emotions, expressing emotions, reasoning, and chess. So, when the astronauts David
Bowman and Frank Poole consider disconnecting HAL's cognitive circuits when he appears to be
mistaken in reporting the presence ofa fault in the spacecraft's com muni cations antenna, HAL
gets nervous. Faced with the prospect of disconnection, HAL decides to kill the astronauts in
order to protect and continue its programmed directives. Its chilling line "I'm sorry Dave, but this
mission is just too important for me to allow you to jeopardize it
1
' made many nervous about the
future of artificial intelligence.
We are not at that point of the development of artificial intelligence yet. However, many
scientists believe that future advances could lead to problems. For example, medical systems can
already interact with patients to simulate empathy. Computer worms and viruses have learned to
vary their structure over time to avoid extermination
. The concern is an "intelligence explosion"
in which smart machines would design even more intelligent machines that humans can neither
understand nor control. This is especially a concern if the tools reach the hands of
criminals.
At
a conferenc
e by the Association for Advancement of Artificial Intelligence, scientists discussed
the issues, the trends and how they could be controlled. There is as yet not agreement among the
researchers, and therefore no guidelines. But, it does give one pause for thought.
210
INTELLIGENCE AND DECISION SUPPORT SYSTEMS
person was a good writer. Through this separation of measures of belief and
disbelief,
it is
possible to present evidence (facts or rules) and measure their impact more directly.
Certainty factors have a range between -1 and 1 and are defined by the difference
between measures of belief and measures of disbelief as shown below:
CF[A,
e\.
= MB
[A,
e]
-
MD[A,
e]
where:
MB [A,
e]
=
measure of increased belief in hypothesis
A
given evidence
e
MD[A,
e]
= measure of increased disbelief in hypothesis
A
given evidence
e
Increments associated with new evidence are made as follows:
MB[A,e] =
1 if
P(h) =
1
max(P(A|e),
P(h)) - P(h)
max(l, 0)
—
p(h)
otherwise
MD[h,e] =
1 if
P(h)
= 0
max(P(A|<?),
P(h)) - P(h)
min(l,0)-/?(A)
otherwise
If
P(h\e) > P(h),
then there is increased confidence in the hypothesis. However, the
paradox that results is
CF(A,e) + CF(A',i>)^ 1
Hence, the confidence in a hypothesis is true given particular evidence and the confidence
the hypothesis is wrong given the evidence does not sum to 1 as it might in probability
theory.
Incrementally acquired evidence is used to update the measures of belief and measures
of disbelief separately:
MB[A,ei&e
2
] =
0
ifMD(A,ei&6>
2
)= 1
MB(A,
si
) + MB(A,
s
2
)[l -
MB(A,
s
x
)]
otherwise
MD[h,ei&e
2
] =
0
MB(h,e
l
&e
2
)=l
MD(A,
si)
+ MD(A,
s
2
)[l -
MD(A,
s
x
)]
otherwise
Furthermore, measures of belief of conjunctions of hypotheses are determined by taking the
minimum value of the measures of belief of the individual hypotheses while measures of
disbelief of conjunctions are determined by taking the maximum value of the measures of
disbelief of the individual hypotheses. Further corrections are taken if there is uncertainty
regarding the certainty of a particular piece of information.
SUGGESTED READINGS
DISCUSSION
Artificial intelligence has two roles in a DSS. First, artificial intelligence can serve as a
model type. In particular, it is an heuristic modeling technique that manipulates symbols
rather than numbers. This kind of modeling is particularly useful when addressing poorly
structured problems or problems for which data are not complete because it replicates the
human reasoning process. A second application of artificial intelligence in a DSS is to
provide intelligent assistance to the users. With the use of artificial intelligence, designers
can build into the DSS expertise the decision maker lachs. This might be with regard to
in modeling, evaluation of alternatives or in postmodeling analysis to improve the quality
of decisions for all users of the system. In order to implement it, designers must codify
the information that experts would use, build procedures for processing that information,
and address the manner by which uncertainty in both information and relationships will be
addressed.
SUGGESTED READINGS
Baker, S.,
The Numerati,
Boston: Houghton-Mifflin, 2008.
Gale,
W. A. (ed.),
Artificial Intelligence and Statistics,
Reading, MA: Addison-Wesley, 1986.
Chou, S. T., H. Hsu, C. Yang, and F. Lai, "A Stock Selection DSS Combining AI and Technical
Analysis,"
Annals of Operations Research,
Vol. 75, January 1997, pp. 335-353.
Delen, D., and R. Sharda, "Artificial Neural Networks in Decision Support Systems," in Burnstein,
F.
and C. W. Holsapple
Handbook on Decision Support Systems,
Vol. I, Berlin: Springer-Verlag,
2008,
pp. 557-580.
Elam, J. J., and B. Konsynski, "Using Artificial Intelligence Techniques to Enhance the
Capabilities of Model Management Systems,"
Decision Sciences,
Vol. 18, No. 3, June 2007,
pp.
487-502.
Gallagher, J. P,
Knowledge Systems for Business: Integrating Expert Systems and MIS,
Englewood
Cliffs,
NJ: Prentice-Hall, 1988.
Harmon, P., and C. Hall,
Intelligent Software Systems Development: An IS Manager's Guide,
New
York: Wiley Professional Computing, 1993.
Hess,
T. J., L. P. Rees, and T. R. Rakes, "Using Autonomous Software Agents in Decision Support
Systems," in Burnstein, F. and C. W. Holsapple (eds).
Handbook on Decision Support Systems,
Vol. I, Berlin: Springer-Verlag, 2008, pp. 529-556.
Kasparov, G., "The Day that I Sensed a New Kind of Intelligence,"
Time
Magazine,
March 25, 1996,
p.
55.
Klahr, P., and D. A. Waterman (Eds.),
Expert Systems:
Techniques,
Tools
and Applications,
Reading,
MA: Addison-Wesley, 1986.
Lewis, D. D., and W. A. Gale, "A Sequential Algorithm for Training Text Classifiers,"
Proceed-
ings of
the
17th Annual International ACM SIGIR Conference on Research and Development in
Information Retrieval,
Dublin, Ireland, 1994, pp. 3-12.
Lucas, P. J. F., "Certainty-Factor-Like Structures in Bayesian Belief Networks,"
Knowledge Based
Systems,
Vol. 14, No. 7, November
2001,
pp. 327-335.
Luger, G., and
W.
Stubblefield,
Artificial Intelligence: Structures and Strategies for
Complex
Problem
Solving,
5th ed., Boston, MA: Benjamin/Cummings, 2004.
McCorduck,
P.,
Machines Who
Think:
A Personal Inquiry into the History and Prospects of Artificial
Intelligence,
New York: A. K. Peters, 2004.
INTELLIGENCE AND DECISION SUPPORT SYSTEMS
Mora, M., G. Forgionne, J. Gupta, F. Cervantes, and O. Gelman, "A Framework to Assess Intelligent
Decision-Making Support
Systems,"
in
Knowledge-Based Intelligent Information and Engineering
Systems,
Berlin: Springer,
2003.
Nilsson, N.,
Artificial Intelligence: A New Synthesis,
San Francisco, CA: Morgan Kaufmann, 1998.
Norman, G. R., L. R. Brooks, C. K. Colle, and R. M. Hatala, "The Benefit of Diagnostic Hypotheses
in Clinical Reasoning: Experimental Study of an Instructional Intervention for Forward and
Backward Reasoning,"
Cognition and Instruction,
Vol. 17, No. 4, February 1999, pp. 433-448.
Pereira, L. M., and A. Saptawijaya, "Modelling Morality with Prospective Logic"
International
Journal of Reasoning-Based Intelligent Systems,
Vol. 1, 2009, pp.
209-221.
Rauch-Hindin, W. B.,
A Guide to Commercial Artificial Intelligence,
Englewood Cliffs: Prentice-
Hall, 1988.
Russell, S. J., and P. Norvig,
Artificial Intelligence: A Modern Approach,
2nd ed., Upper Saddle
River, NJ: Prentice-Hall,
2003.
Sauter, V. L., and L. A. Madeo, "Using Statistics to Make Expert Systems User-Acquainted,"
Annals
of Mathematics and Artificial Intelligence,
Vol. 2, 1990, pp. 309-326.
Turban, E., and H. J. Watson, " Integrating Expert Systems, Executive Information Systems and
Decision Support
Systems,"
in
P.
Gray
(Ed.),
Decision Support and Executive Information Systems,
Englewood Cliffs, NJ: Prentice-Hall, 1994, pp. 399-408.
Waterman, D. A.,
A Guide to Expert Systems,
Reading, MA: Addison-Wesley, 1985.
QUESTIONS
1.
What are certainty factors? What are the risks and advantages of their use?
2.
Discuss the difference between the concepts of
"disbelief"
and "the lack of belief in
decision making and the role these concepts play in selecting an automobile. What is
the implication for building certainty factors into the system?
3.
Historically, what were the differences between decision support and expert systems?
What factors led to the narrowing of those differences? What implication does this have
for the model management feature in DSS?
4.
How do you know when you have included enough "intelligence" in a decision support
system?
5.
Compare and contrast symbolic processing and numerical processing. Why is the former
referred to as "intelligence"?
6. What factors would lead you to recommend selecting a project that is appropriate for
expert systems development?
7.
Consider preparing a DSS for which certainty factors are relevant. This may be a class
project, an example with which you are familiar, or a hypothetical example. What issues
need to be tracked with certainty factors?
ON THE WEB
On the Web
for this supplement to Chapter 4 provides additional information about artificial
intelligence and how it applies to DSS design. Links can provide access to demonstration
packages, general overview information, applications, software providers, tutorials, and
ON
THE WEB
213
more. Additional discussion questions and new applications will also be added as they
become available.
•
Links to applications
are
provided.
Since artificial intelligence can be nebulous until
actual applications are addressed, there are links that can provide descriptions of
applications. Information is available regarding stand-alone applications, integrated
tools,
business-related systems, such as intelligent agents, and general interest ap-
plications, such as chess programs.
•
Links
to
press accounts of the use of intelligence are available.
These links provide
general overviews of uses in business, industry areas, government, and society as a
whole. Furthermore, links can be made to interviews with experts and their forecasts
about future use.
•
Links provide access to examples of
artificial
intelligence tools.
Links provide ac-
cess to product information, product comparisons and reviews, as well as general
information about both artificial intelligence and expert systems tools.
•
Links provide access to more examples of how to build intelligence into a DSS.
Both conceptual examples and actual code are available to add to the material in the
chapter supplement.
You can access material for this supplement to Chapter 4 from the general Web page for
the book or directly at http://www.umsl.edu/^sauterv/DSS4BI/mbms_sup.html.
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